def test_calc_likelihoods_1():
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
av_image = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 2, 1, 0],
[0, 1, 1, 1, 0], [0, 0, 0, 0, 0]])
#av_image_error = np.random.normal(0.1, size=av_image.shape)
av_image_error = 0.1 * av_image
#nhi_image = av_image + np.random.normal(0.1, size=av_image.shape)
nhi_image = 5 * av_image
# add intercept
intercept_answer = 0.7
av_image = av_image + intercept_answer
width_grid = np.arange(0, 10, 0.1)
dgr_grid = np.arange(0, 2, 0.1)
intercept_grid = np.arange(-2, 2, 0.1)
vel_center = 1
results = \
cloudpy._calc_likelihoods(
nhi_image=nhi_image,
av_image=av_image,
av_image_error=av_image_error,
dgr_grid=dgr_grid,
intercept_grid=intercept_grid,
)
dgr_answer = 1 / 5.0
assert_almost_equal(results['intercept_max'], intercept_answer)
assert_almost_equal(results['dgr_max'], dgr_answer)
def test_calc_likelihoods_1():
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
av_image = np.array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 2, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]])
#av_image_error = np.random.normal(0.1, size=av_image.shape)
av_image_error = 0.1 * av_image
#nhi_image = av_image + np.random.normal(0.1, size=av_image.shape)
nhi_image = 5 * av_image
# add intercept
intercept_answer = 0.7
av_image = av_image + intercept_answer
width_grid = np.arange(0, 10, 0.1)
dgr_grid = np.arange(0, 2, 0.1)
intercept_grid = np.arange(-2, 2, 0.1)
vel_center = 1
results = \
cloudpy._calc_likelihoods(
nhi_image=nhi_image,
av_image=av_image,
av_image_error=av_image_error,
dgr_grid=dgr_grid,
intercept_grid=intercept_grid,
)
dgr_answer = 1/5.0
assert_almost_equal(results['intercept_max'], intercept_answer)
assert_almost_equal(results['dgr_max'], dgr_answer)
def test_calc_likelihoods_2():
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
from myimage_analysis import calculate_nhi
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.axes_grid1 import ImageGrid
av_image = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 2, 1, 0],
[np.nan, 1, 1, 1, 0], [0, 0, 0, 0, 0]])
#av_image_error = np.random.normal(0.1, size=av_image.shape)
av_image_error = 0.1 * np.ones(av_image.shape)
#nhi_image = av_image + np.random.normal(0.1, size=av_image.shape)
hi_cube = np.zeros((5, 5, 5))
# make inner channels correlated with av
hi_cube[:, :, :] = np.array([
[
[1., 0., 0., 0., 0.],
[np.nan, 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[1., 0., 0., 0., 10.],
],
[
[0., 0., 0., 0., 0.],
[0., 0., 2., 0., 0.],
[0., 0., 4., 0., 0.],
[0., 0., 2., 0., 0.],
[0., 0., 0., 0., 0.],
],
[
[0., 0., 0., 0., 0.],
[0., 0., 0., 2., 0.],
[0., 0., 0., 2., 0.],
[0., 0., 0., 2., np.nan],
[0., 0., 0., 0., 0.],
],
[
[0., 0., 0., 0., 0.],
[0., 2., 0., 0., 0.],
[0., 2., 0., 0., 0.],
[0., 2., 0., 0., 0.],
[0., 0., 0., 0., 0.],
],
[
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., np.nan],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.2],
],
])
if 1:
fig = plt.figure(figsize=(4, 4))
imagegrid = ImageGrid(fig, (1, 1, 1),
nrows_ncols=(1, 5),
ngrids=5,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
for i in xrange(5):
im = imagegrid[i].imshow(
hi_cube[i, :, :],
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/hi_cube.png')
# make edge channels totally uncorrelated
#hi_cube[(0, 4), :, :] = np.arange(0, 25).reshape(5,5)
#hi_cube[(0, 4), :, :] = - np.ones((5,5))
hi_vel_axis = np.arange(0, 5, 1)
# add intercept
intercept_answer = 0.9
av_image = av_image + intercept_answer
if 1:
fig = plt.figure(figsize=(4, 4))
params = {
'figure.figsize': (1, 1),
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
imagegrid = ImageGrid(fig, (1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
im = imagegrid[0].imshow(
av_image,
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/av.png')
width_grid = np.arange(0, 5, 1)
dgr_grid = np.arange(0, 1, 0.1)
intercept_grid = np.arange(-1, 1, 0.1)
vel_center = 2
results = \
cloudpy._calc_likelihoods(
hi_cube=hi_cube / 1.832e-2,
hi_vel_axis=hi_vel_axis,
vel_center=vel_center,
av_image=av_image,
av_image_error=av_image_error,
width_grid=width_grid,
dgr_grid=dgr_grid,
intercept_grid=intercept_grid,
)
dgr_answer = 1 / 2.0
width_answer = 2
width = results['width_max']
dgr = results['dgr_max']
intercept = results['intercept_max']
print width
if 0:
width = width_answer
intercept = intercept_answer
dgr = dgr_answer
vel_range = (vel_center - width / 2.0, vel_center + width / 2.0)
nhi_image = calculate_nhi(cube=hi_cube,
velocity_axis=hi_vel_axis,
velocity_range=vel_range) / 1.823e-2
if 1:
fig = plt.figure(figsize=(4, 4))
imagegrid = ImageGrid(fig, (1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
im = imagegrid[0].imshow(
nhi_image,
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/nhi.png')
if 1:
fig = plt.figure(figsize=(4, 4))
imagegrid = ImageGrid(fig, (1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
im = imagegrid[0].imshow(
nhi_image * dgr + intercept,
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/av_model.png')
print('residuals = ')
print(av_image - (nhi_image * dgr + intercept))
print('dgr', dgr)
print('intercept', intercept)
print('width', width)
assert_almost_equal(results['intercept_max'], intercept_answer)
assert_almost_equal(results['dgr_max'], dgr_answer)
assert_almost_equal(results['width_max'], width_answer)
def test_calc_likelihoods_2():
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
from myimage_analysis import calculate_nhi
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.axes_grid1 import ImageGrid
av_image = np.array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 2, 1, 0],
[np.nan, 1, 1, 1, 0],
[0, 0, 0, 0, 0]])
#av_image_error = np.random.normal(0.1, size=av_image.shape)
av_image_error = 0.1 * np.ones(av_image.shape)
#nhi_image = av_image + np.random.normal(0.1, size=av_image.shape)
hi_cube = np.zeros((5, 5, 5))
# make inner channels correlated with av
hi_cube[:, :, :] = np.array(
[
[[ 1., 0., 0., 0., 0.],
[ np.nan, 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 1., 0., 0., 0., 10.],],
[[ 0., 0., 0., 0., 0.],
[ 0., 0., 2., 0., 0.],
[ 0., 0., 4., 0., 0.],
[ 0., 0., 2., 0., 0.],
[ 0., 0., 0., 0., 0.],],
[[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 2., 0.],
[ 0., 0., 0., 2., 0.],
[ 0., 0., 0., 2., np.nan],
[ 0., 0., 0., 0., 0.],],
[[ 0., 0., 0., 0., 0.],
[ 0., 2., 0., 0., 0.],
[ 0., 2., 0., 0., 0.],
[ 0., 2., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],],
[[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., np.nan],
[ 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0.],
[ 1., 0., 0., 0., 0.2],],
]
)
if 1:
fig = plt.figure(figsize=(4,4))
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=(1,5),
ngrids=5,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
for i in xrange(5):
im = imagegrid[i].imshow(hi_cube[i, :, :],
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/hi_cube.png')
# make edge channels totally uncorrelated
#hi_cube[(0, 4), :, :] = np.arange(0, 25).reshape(5,5)
#hi_cube[(0, 4), :, :] = - np.ones((5,5))
hi_vel_axis = np.arange(0, 5, 1)
# add intercept
intercept_answer = 0.9
av_image = av_image + intercept_answer
if 1:
fig = plt.figure(figsize=(4,4))
params = {
'figure.figsize': (1, 1),
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=(1,1),
ngrids=1,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
im = imagegrid[0].imshow(av_image,
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/av.png')
width_grid = np.arange(0, 5, 1)
dgr_grid = np.arange(0, 1, 0.1)
intercept_grid = np.arange(-1, 1, 0.1)
vel_center = 2
results = \
cloudpy._calc_likelihoods(
hi_cube=hi_cube / 1.832e-2,
hi_vel_axis=hi_vel_axis,
vel_center=vel_center,
av_image=av_image,
av_image_error=av_image_error,
width_grid=width_grid,
dgr_grid=dgr_grid,
intercept_grid=intercept_grid,
)
dgr_answer = 1/2.0
width_answer = 2
width = results['width_max']
dgr = results['dgr_max']
intercept = results['intercept_max']
print width
if 0:
width = width_answer
intercept = intercept_answer
dgr = dgr_answer
vel_range = (vel_center - width / 2.0, vel_center + width / 2.0)
nhi_image = calculate_nhi(cube=hi_cube,
velocity_axis=hi_vel_axis,
velocity_range=vel_range) / 1.823e-2
if 1:
fig = plt.figure(figsize=(4,4))
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=(1,1),
ngrids=1,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
im = imagegrid[0].imshow(nhi_image,
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/nhi.png')
if 1:
fig = plt.figure(figsize=(4,4))
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=(1,1),
ngrids=1,
cbar_mode="single",
cbar_location='top',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
aspect=True,
label_mode='L',
share_all=True)
cmap = cm.get_cmap('Greys', 5)
im = imagegrid[0].imshow(nhi_image * dgr + intercept,
origin='lower',
#aspect='auto',
cmap=cmap,
interpolation='none',
vmin=0,
vmax=4)
#cb = imagegrid[i].cax.colorbar(im)
cbar = imagegrid.cbar_axes[0].colorbar(im)
#plt.title('HI Cube')
plt.savefig('/usr/users/ezbc/Desktop/av_model.png')
print('residuals = ')
print(av_image - (nhi_image * dgr + intercept))
print('dgr', dgr)
print('intercept', intercept)
print('width', width)
assert_almost_equal(results['intercept_max'], intercept_answer)
assert_almost_equal(results['dgr_max'], dgr_answer)
assert_almost_equal(results['width_max'], width_answer)
